Get conversation patterns

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint, and openWorldHint, so the safety profile is covered without the description. The description adds the Studio data source and return format, but 'Read-only' merely repeats annotations and no richer behavioral context is given.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three compact sentences with no wasted language. Purpose, alternative tool routing, safety, and return format are front-loaded and easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter read-only tool with full schema coverage, annotations, and an output schema, the description covers the essential call context. It could say more about what kinds of conversation patterns are included, but that gap is reduced by the output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the optional model_id field is already fully documented in the schema. The description reinforces the one-model-or-all-models behavior but does not add syntax or filtering semantics beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: summarise conversation patterns from Studio data. It also distinguishes scope (one model or all models) and separates the tool from get_model_performance_metrics, which covers speed and token counts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly names the alternative get_model_performance_metrics and the condition for choosing it: speed and token counts. It also clarifies that this tool is for conversation patterns and supports either one model or all models.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.